Real-time full body motion imitation on the COMAN humanoid robot

Gams, Andrej; Kieboom, Jesse van den; Dzeladini, Florin; Ude, Aleš; Ijspeert, Auke Jan

doi:10.1017/s0263574714001477

Cited by 20 publications

(8 citation statements)

References 27 publications

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“…It may then be feasible to restrict the prior distributions to generate stable dynamic matrices within the learning algorithm. An additional direction of research is to further constrain the motion based on the robot balance stability using the center of mass Jacobian [35].…”

Section: Discussionmentioning

confidence: 99%

“…To ensure that the robot maintains balance in relation to positioning of the zero moment point, one can modulate the synthesized trajectory using the center of mass Jacobian with a similar prioritized control scheme [35]. Including whole body balance control was beyond the scope of this paper.…”

Section: B Robot Correspondence Mappingmentioning

confidence: 99%

“…13, we show samples of the robot motion driven by the model. The robot becomes unstable on the foot lift-off phase of the cycle, however, the trajectory may be modified with a task prioritized control of the center of mass Jacobian [35]. …”

Section: Multiple Walking Stylesmentioning

confidence: 99%

See 2 more Smart Citations

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

Field

Stirling

Pan

et al. 2016

IEEE Trans. Cybern.

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This paper presents an approach for learning robust models of humanoid robot trajectories from demonstration. In this formulation, a model of the joint space trajectory is represented as a sequence of motion primitives where a nonlinear dynamical system is learned by constructing a hidden Markov model (HMM) predicting the probability of residing in each motion primitive. With a coordinated mixture of factor analyzers as the emission probability density of the HMM, we are able to synthesize motion from a dynamic system acting along a manifold shared by both demonstrator and robot. This provides significant advantages in model complexity for kinematically redundant robots and can reduce the number of corresponding observations required for further learning. A stability analysis shows that the system is robust to deviations from the expected trajectory as well as transitional motion between manifolds. This approach is demonstrated experimentally by recording human motion with inertial sensors, learning a motion primitive model and correspondence map between the human and robot, and synthesizing motion from the manifold to control a 19 degree-of-freedom humanoid robot.

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Section: Discussionmentioning

confidence: 99%

Section: B Robot Correspondence Mappingmentioning

confidence: 99%

See 1 more Smart Citation

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

Field

Stirling

Pan

et al. 2016

IEEE Trans. Cybern.

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show abstract

“…An online optimization of the footsteps placement for gait generation for humanoid walking in the presence of external disturbances likes pushes was presented in [1]. Real-time imitation of full-body motion of human demonstration on humanoid robot COMAN while maintaining stability was presented in [13]. The imitation motions of COMAN where constrained using prioritized task control to maintain stability.…”

Section: Related Workmentioning

confidence: 99%

Climbing over large obstacles with a humanoid robot via multi-contact motion planning

Kanajar

Caldwell

Kormushev

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.

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“…The control sets the COM to remain in the center between the feet. For details on the implementation of the controller refer to [26].…”

Section: A Balance Controlmentioning

confidence: 99%

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Gams

Kieboom

Vespignani

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle.

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Real-time full body motion imitation on the COMAN humanoid robot

Cited by 20 publications

References 27 publications

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers

Climbing over large obstacles with a humanoid robot via multi-contact motion planning

Rich periodic motor skills on humanoid robots: Riding the pedal racer

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